Data in transit validation for cloud computing using cloud-based algorithm detection of injected objects
The recent paradigm shift in the IT sector leading to cloud computing however innovative had brought along numerous data security concerns. One major such security laps is that referred to as the Man in the Middle (MITM) attack where external data are injected to either hijack a data in transit o...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Institute of Advanced Engineering and Science (IAES)
2018
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/64152/ http://irep.iium.edu.my/64152/ http://irep.iium.edu.my/64152/ http://irep.iium.edu.my/64152/1/64152_Data%20in%20transit%20validation%20for%20cloud%20computing_article.pdf http://irep.iium.edu.my/64152/2/64152_Data%20in%20transit%20validation%20for%20cloud%20computing_scopus.pdf |
| Summary: | The recent paradigm shift in the IT sector leading to cloud computing
however innovative had brought along numerous data security concerns. One
major such security laps is that referred to as the Man in the Middle (MITM)
attack where external data are injected to either hijack a data in transit or to
manipulate the files and object by posing as a floating cloud base. Fresh
algorithms’ for cloud data protection do exist however, they are still prone to
attack especially in real-time data transmissions due to employed
mechanism. Hence, a validation protocol algorithm based on hash function
labelling provides a one-time security header for transferable files that
protects data in transit against any unauthorized injection. The labelling
header technique allows for a two-way data binding; DOM based
communication between local and cloud computing that triggers automated
acknowledgment immediately after file modification. A two layer encryption
functions in PHP was designed for detecting injected object; bcrypt methods
in Laravel and MD5 that generate 32 random keys. A sum total of 1600
different file types were used during training then evaluation of the proposed
algorithm, where 87% of the injected objects were correctly detected. |
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